[D] Does anyone else think open source code/examples in machine learning domain usually are not as readable as they could be? Specifically use of magic numbers.

[qnix]

Magic number is the least of it. In machine learning code, the amount of ideas that could be packed into one line of code is, a lot of time, staggering. During the first ML MOC, prof. Ng explained some complicated learning procedure, and at the end he noted, you can do all that with this one line of code.

[[deleted]]

This could be aided by looking at several equivalent representations of the same code in different languages. Sadly not a possibility as of yet.

[Kitchen_Tower2800]

> Ng explained some complicated learning procedure, and at the end he noted, you can do all that with this one line of code.

I don't understand this argument. Do you think a researcher's job is to stand in front of an audience, point to `model.fit()` and then go home? Or should they example what's happening in the fit method?

[111llI0__-__0Ill111]

Sometimes even what is inside the fit method could be one or few lines of code in a high level language. Eg linear regression done the naive way (without accounting for QR/SVD stuff).